Network Structure. The shape estimation network is an autoencoder consisting of \(n\)  Residual-Recursive (RR) modules. The overall framework is shown in Fig. REF . In each RR module, the feature is first fed into a recursive layer with residual connection, then it is processed with a fully connected layer where the number of output channels is reduced to half. The details of RR module is illustrated in Fig. REF . After a middle layer, the feature is mapped to a 3D shape with Inverse Residual-Recursive modules. The two types of RR modules are illustrates in Fig. REF . A tied-weight strategy [1]} is used here. Finally, the 3D shape is produced by a shape layer. Motivated by previous works of NRSfM where the compactness of model is emphasized [2]}, [3]}, we choose the residual-recursive structure [4]}, [5]}, [6]} to enhance the representation power of a standard convolution layer without increasing the number of parameters. We empirically find that this structure is more effective than a standard convolution layer in an autoencoder for learning difficult 2D-3D mapping.
